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Keywords: control of computing systems, adaptive control, cloud computing, ing predictive techniques to estimate the future load of a service. enforced to either the cloud service or to the monitoring topology. Furthermore, it is ing on-demand virtualized infrastructure in a pay-as-you-use model . Individual nodes in the cloud use email@example.com). adaptive middleware and Moreover, we view Service Clouds as ing sections, we present three case.
The proposed scheduler processing time. Finally, Eq. However, it performs Before proceeding, we explicitly point out that rmax the following reconfiguration actions see Fig.
In the typically dictated by the WiFi technology actually employed remaining part of this section, we deal with the resource for implementing the single-hop wireless link of Fig. From Eqs.
Hence, the following result problem in Proposition 1. Feasibility conditions. Proposition 3. Solution of the resource reconfiguration problem. Under the reported framework, the following four inequalities: Under the feasibility conditions in It may be adaptively updated according to the following gradient-based projection iteration: Proposition 2. Hard QoS guarantees. Thus, the following QoS guarantees hold: The interval I is the set of the desired algebraic equation in Hence, at each consolidation form : The first term runs at the beginning of the considered slot t.
The New Rules of Talent Management
Furthermore, in 23 accounts for the decrement resp. L t , which is done with respect to the variable x and is Third, turning ON a VM induces a latency of TON evaluated at the resource configuration setting attained at seconds .
Consider that, due to the presence of the step-size function in 23 , On the basis of the above three remarks, the resource the previously stated resource consolidation problem resists, indeed, consolidation problem is formulated as follows: Main tasks of the intra-Fog Virtualization layer of Fig.
First, it must guarantee that the demands for the tive implementation of the proposed resource scheduler. Second, it must profile at runtime Algorithm 1 A pseudo-code of the proposed adaptive re- the maximum and idle energies actually consumed by the source scheduler running VMs.
Perform resource consolidation by running the itera- layer of Fig.
Cloud computing architecture
Update the sets S t and S t ; schedulers , . Interestingly, Table 1 of  points 6: Evaluate Eqs. Likewise, the Compute U t through Eq. For this purpose, SecondNet implements a suitable Port-Switching based Source Routing algorithm, that may directly work at the Middleware layer of Fig. Regarding the aforementioned second task, the max- First, since the iterations in 25 are carried out at the imum and idle energies consumed by each VM may be beginning of each slot, the iteration index n must run faster profiled at runtime by resorting to the so-called Joulemeter than the slot time TS.
Hence, the actual time duration: TI s tool . It is a software tool that operates at the Virtual- of each n-indexed iteration must be so small to allow the ization layer of Fig.
At this regard, the formal results of Theorem 3. For this purpose, Joulemeter  assure the asymptotic convergence of the iterations in uses hypervisor-observable hardware power states to track Furthermore, we have numerically ascertained that, at the VM energy usage on each hardware component see least in the tests of Section 6, about n-indexed steps Section 5 of  for a detailed description of the Joulemeter suffice, in order to attain the convergence of 25 with a final tool.
Hence, in the carried out tests, we pose: Therefore, VM. As a consequence, the per-VM implementation world input arrival traces, and, then, compares it with the complexity of the proposed scheduler does not depend on corresponding ones of the DVFS-based scheduler in , the number M of the available VMs, that is, it is of the order Lyapunov-based scheduler in , static scheduler in  of O 1.
Overall, the above remarks lead to the conclusion that the implementation of the proposed scheduler is adaptive 6.
They emulate the energy possibly, large size the considered Fog node. Default values of the main simulated parameters. All the emulated servers are connected of served vehicles N vc may be numerically evaluated as in through commodity Giga Ethernet Network Interface Cards , : The second set of simulations in Fig.
They move along the highway of Fig. Since no any assumption has been introduced about the 12 statistical behavior of the state in 10 of the considered RSU-to-vehicle mobile connection, the optimality of the 10 5. E tot -vs. However, ii being the vehicle speeds evenly distributed and subject the interesting point is that, even for increasing values of to random spatial fluctuations, the sojourn times of the the available Eave , the admission control performed by the vehicles in each cluster are geometrically distributed r.
According to this model, per-vehicle transitions among adjacent spatial clus- 6. Furthermore, the per-vehicle inter-cluster average fraction: F A and T tot versus Eave for the application scenario of Section 6.
HR Goes Agile
For this purpose, The corresponding the tradeoff between admission capability and induced de- numerically evaluated performance is reported in Table 2. Specifically, two main con- An examination of these results leads to two main clusions may be drawn from the plots of Figs. Thus, in order to offset this effect without T tot penalizing the energy performance, the scheduler requires 1 10 more buffering capacity, that, in turn, penalizes the resulting delay performance see the last column of Table 2.
A of Section 6. The numerical trials of this sub-section aim at testing both the actual convergence to the steady-state and correspond- ing convergence speed of the proposed iterative consolida- 6. For this purpose, we have eval- Goal of this set of numerical tests is to acquire insight about uated and compared its performance against the optimal the effects induced by the vehicle mobility on the resulting one. Buffer size—vs. Third, since the rate of the occurrence of VM underuti- lization phenomena increases for increasing values of ke and too frequent occurrence of VM underutilization phenomena 10 tends to increase the resource reconfiguration actions to be carried out at the consolidation instants, the energy penalty suffered by the proposed consolidation algorithm tends to 5 somewhat increases for growing values of ke.
The plots of Fig. A comparison of the plots of t Fig. The corresponding PMR Case 1 and time-correlation coefficient are 2. Case 2 0. The performance U t comparisons have been carried out by implementing three different event-driven policies, in which consolidation at 0. Average energy loss in percent of the proposed 3. The appli- cation scenario of Section 6. An examination of the results of Table 3 leads to three 6. First, the actual rate of occurrence of In this sub-section, we compare the energy performance of consolidation events increases by passing from Case 1 to the proposed adaptive scheduler against the corresponding Case 3.
This induces, in turn, a reduction in the performance ones of some state-of-the-art schedulers, e. Goal of this last set of c IEEE. This is motivated by the fact that current data cen- delay. Actual performance of the proposed scheduler has ters usually rely on static resource provisioning, so that, been numerically tested under both synthetic and real- by design, a fixed number of VMs constantly runs at the world input traffic, various mobility conditions and settings maximum processing rate fmax , in order to provide the of the networked Fog platform.
This work can be extended computing capacity needed to satisfy the peak input traffic in some directions of potential interest. Just as an example, amax . Although the resulting static scheduler does not closed networked multi-tier computing infrastructures may suffer by the energy costs arising from the adaptive resource be considered for the support of delay-tolerant session- scaling and consolidation, it induces overbooking of the based services .
Since, in this application scenario, intra- used resources. Hence, the average energy consumption: Optimizing live migration of VMs without resorting In order to carry out fair energy comparisons, in the to exhaustive NP-hard numerical approaches could be an tests of this sub-section, we have implemented the proposed interesting topic for future work.
Whaiduzzaman, M. Sookhak, A. Gani, and R.
Hence, Computer Applications, vol. Karagiannis, O. Altintas, E. Ekici, G. Heijenk, B.
Jarupan, K. Lin, on the basis of the carried out numerical tests, we have and T. A survey and tutorial on experienced that: Abolfazli, Z. Sanaei, and I. Baccarelli, N. Cordeschi, A. Mei, M. Panella, M. Shojafar, and is provided by the performed consolidation actions. These J. Mouftah and B. Kantarci, Communication Infrastructures for Finally, in order to evaluate the energy reduction in- Cloud Computing.
IGI Global, Hajibaba and S. Table 4 reports the obtained vol. Suryawanshi and G. Cordeschi, T. Patriarca, and E. This confirms that the proposed scheduler is capable  C.
Wu, R. Chang, and H. Cordeschi, M. Shojafar, and E.
Urgaonkar, U. Kozat, K. Igarashi, and M. Balakrishnan and C. The overall goal is Cloud Computing, , pp. Song, Y. Cui, M. Li, J. Qiu, and R. Miettinen and J. Balasubramanian, A. Balasubramanian, and A.
Average computing-plus-communication energy consumptions under the application scenario of Section 6. Huang, F. Qian, A. Gerber, Z. Mao, S. Sen, and O. Spatscheck,  S. Sanaei, M. Alizadeh, A. Gani, and F. Festag, G. Noecker, M. Strassberger, A. Bochow, Mohammad Shojafar is currently last year M.
And Weitzman-Garcia says the benefits to the organization far outweigh the costs to HR. DigitalOcean, a New York—based start-up focused on software as a service SaaS infrastructure, engages a full-time professional coach on-site to help all managers give better feedback to employees and, more broadly, to develop internal coaching capabilities.
The idea is that once one experiences good coaching, one becomes a better coach. Not everyone is expected to become a great coach—those in the company who prefer coding to coaching can advance along a technical career track—but coaching skills are considered central to a managerial career. Traditional HR focused on individuals—their goals, their performance, their needs.
But now that so many companies are organizing their work project by project, their management and talent systems are becoming more team focused. Groups are creating, executing, and revising their goals and tasks with scrums—at the team level, in the moment, to adapt quickly to new information as it comes in. It comes from rugby, where players pack tightly together to restart play. They are also taking it upon themselves to track their own progress, identify obstacles, assess their leadership, and generate insights about how to improve performance.
In that context, organizations must learn to contend with: Multidirectional feedback. Peer feedback is essential to course corrections and employee development in an agile environment, because team members know better than anyone else what each person is contributing.
That keeps input constructive and prevents the undermining of colleagues that sometimes occurs in hypercompetitive workplaces. But some executives believe that peer feedback should have an impact on performance evaluations. Employees may choose whether to include managers and others in their comments to peers. The risk of cutthroat behavior is mitigated by the fact that peer comments to the supervisor also go to the team. Anyone trying to undercut colleagues will be exposed. They started with periodic confidential employee surveys and focus groups to discover which issues people wanted to discuss with their managers.
HR then distilled that data for supervisors to inform their conversations with direct reports. Mitre also learned that the most critical factor in getting subordinates to be candid was having managers explicitly say that they wanted and appreciated comments.
As with any employee survey, soliciting upward feedback and not acting on it has a diminishing effect on participation; it erodes the hard-earned trust between employees and their managers. A revised system for upward feedback will roll out this year.
Because feedback flows in all directions on teams, many companies use technology to manage the sheer volume of it. Apps allow supervisors, coworkers, and clients to give one another immediate feedback from wherever they are. In some apps, employees and supervisors can score progress on goals; at least one helps managers analyze conversations on project management platforms like Slack to provide feedback on collaboration.
Such tools enable managers to see fluctuations in individual performance over time, even within teams.
We know that companies recognize and reward improvement as well as actual performance, however, so hiding problems may not always pay off for employees. Frontline decision rights. The fundamental shift toward teams has also affected decision rights: Organizations are pushing them down to the front lines, equipping and empowering employees to operate more independently. So the bank embedded agile coaches in business teams.
These are the agile version of after-action reviews; their purpose is to keep improving processes. Because the retrospectives quickly identified concrete successes, failures, and root causes, senior leaders at BMO immediately recognized their value, which helped them get on board with agile generally and loosen their grip on decision making.
Complex team dynamics. It uses an enterprise-wide platform called Team Space, which tracks data on team projects, needs, and achievements to both measure and improve what teams are doing within units and across the company. Pay is changing as well. Research and practice have shown that compensation works best as a motivator when it comes as soon as possible after the desired behavior.
Instant rewards reinforce instant feedback in a powerful way. Annual merit-based raises are less effective, because too much time goes by. Patagonia has actually eliminated annual raises for its knowledge workers. Instead the company adjusts wages for each job much more frequently, according to research on where market rates are going.
Increases can also be allocated when employees take on more-difficult projects or go above and beyond in other ways. Upward feedback from employees to team leaders is valued in agile organizations. Compensation is also being used to reinforce agile values such as learning and knowledge sharing. In the start-up world, for instance, the online clothing-rental company Rent the Runway dropped separate bonuses, rolling the money into base pay. CEO Jennifer Hyman reports that the bonus program was getting in the way of honest peer feedback.
DigitalOcean redesigned its rewards to promote equitable treatment of employees and a culture of collaboration. Salary adjustments now happen twice a year to respond to changes in the outside labor market and in jobs and performance. More important, DigitalOcean has closed gaps in pay for equivalent work. To personalize compensation, the firm maps where people are having impact in their roles and where they need to grow and develop.
Negotiating to raise your own salary is fiercely discouraged. All employees are eligible for bonuses, which are based on company performance rather than individual contributions.
How does DigitalOcean motivate people to perform their best without inflated financial rewards? Matt Hoffman, its vice president of people, says it focuses on creating a culture that inspires purpose and creativity.
So far that seems to be working. The latest engagement survey, via Culture Amp, ranks DigitalOcean 17 points above the industry benchmark in satisfaction with compensation.
With the improvements in the economy since the Great Recession, recruiting and hiring have become more urgent—and more agile. For instance, a cross-functional team works together on all hiring requisitions. Openings are ranked, and the team concentrates on the top-priority hires until they are completed. It works on several hires at once so that members can share information about candidates who may fit better in other roles.
The team keeps track of its cycle time for filling positions and monitors all open requisitions on a kanban board to identify bottlenecks and blocked processes. IBM now takes a similar approach to recruitment. Companies are also relying more heavily on technology to find and track candidates who are well suited to an agile work environment. The IT recruiting company HackerRank offers an online tool for the same purpose. Learning and development.
Most companies already have a suite of online learning modules that employees can access on demand.
Fuzzy ACID properties for self‐adaptive composite cloud services execution
Although helpful for those who have clearly defined needs, this is a bit like giving a student the key to a library and telling her to figure out what she must know and then learn it. Newer approaches use data analysis to identify the skills required for particular jobs and for advancement and then suggest to individual employees what kinds of training and future jobs make sense for them, given their experience and interests.
What HR Can Learn from Tech The agile pioneers in the tech world are years ahead of everyone else in adopting the methodology at scale. So who better to provide guidance as managers and HR leaders grapple with how to apply agile talent practices throughout their organizations?
In a recent survey, thousands of software developers across many countries and industries identified their biggest obstacles in scaling and the ways they got past them.Remember me on this computer. The Distributed Composite Services layer other node. EC2 associates an elastic IP address with start as soon as several MP3 frames have been gen- an account; this mechanism allows a user to mask erated; another question is if the converted music the failure of an instance and re-map a public IP file should be saved for later use or discarded.
Brudno, and M. International Conference on, june , pp. Peer feedback is essential to course corrections and employee development in an agile environment, because team members know better than anyone else what each person is contributing. The Mobile device may not be aware of this virtual network of Surrogates and may receive the results of its offloaded computation from any one of the participating Surrogates.