232 research outputs found
Optimizing egalitarian performance in the side-effects model of colocation for data center resource management
In data centers, up to dozens of tasks are colocated on a single physical
machine. Machines are used more efficiently, but tasks' performance
deteriorates, as colocated tasks compete for shared resources. As tasks are
heterogeneous, the resulting performance dependencies are complex. In our
previous work [18] we proposed a new combinatorial optimization model that uses
two parameters of a task - its size and its type - to characterize how a task
influences the performance of other tasks allocated to the same machine.
In this paper, we study the egalitarian optimization goal: maximizing the
worst-off performance. This problem generalizes the classic makespan
minimization on multiple processors (P||Cmax). We prove that
polynomially-solvable variants of multiprocessor scheduling are NP-hard and
hard to approximate when the number of types is not constant. For a constant
number of types, we propose a PTAS, a fast approximation algorithm, and a
series of heuristics. We simulate the algorithms on instances derived from a
trace of one of Google clusters. Algorithms aware of jobs' types lead to better
performance compared with algorithms solving P||Cmax.
The notion of type enables us to model degeneration of performance caused by
using standard combinatorial optimization methods. Types add a layer of
additional complexity. However, our results - approximation algorithms and good
average-case performance - show that types can be handled efficiently.Comment: Author's version of a paper published in Euro-Par 2017 Proceedings,
extends the published paper with addtional results and proof
Power Modeling and Resource Optimization in Virtualized Environments
The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm
Scheduling Problems
Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems
Scheduling for Service Stability and Supply Chain Coordination
This dissertation studies scheduling for service stability and for supply chain coordination as well. The scheduling problems for service stability are studied from the single perspective of a firm itself, while the scheduling problems for supply chain coordination are investigated from the perspective of a supply chain. Both the studies have broad applications in real life.
In the first study, several job scheduling problems are addressed, with the measure of performance being job completion time variance (CTV). CTV minimization is used to represent service stability, since it means that jobs are completed in a relative concentrated period of time. CTV minimization also conforms to the Just-in-time philosophy. Two scheduling problems are studied on multiple identical parallel machines. The one problem does not restrict the idle times of machines before their job processing, while the other does. For these two scheduling problems, desirable properties are explored and heuristic algorithms are proposed. Computational results show the excellent performances of the proposed algorithms. The third scheduling problem in the first study is considered on a single machine and from the users’ perspective rather than the system’s perspective. The performance measure is thus class-based completion time variance (CB-CTV). This problem is shown to be able to be transformed into multiple CTV problems. Therefore, the well-developed desirable properties of the CTV problem can be applied to solve the CB-CTV problem. The tradeoff between the CB-CTV problem and the CTV problem is also investigated.
The second study deals with scheduling coordination in a supply chain, since supply chain coordination is increasingly critical in recent years. Usually, different standpoints prevent decision makers in a supply chain from having agreement on a certain scheduling decision. Therefore conflicts arise. In pursuit of excellent performance of the whole supply chain, coordination among decision makers is needed. In this study, the scheduling conflicts are measured and analyzed from different perspectives of decision makers, and cooperation mechanisms are proposed based on different scenarios of the relative bargaining power among decision makers. The cooperation savings are examined as well
Scheduling on parallel machines with a common server in charge of loading and unloading operations
This paper addresses the scheduling problem on two identical parallel
machines with a single server in charge of loading and unloading operations of
jobs. Each job has to be loaded by the server before being processed on one of
the two machines and unloaded by the same server after its processing. No delay
is allowed between loading and processing, and between processing and
unloading. The objective function involves the minimization of the makespan.
This problem referred to as P2, S1|sj , tj |Cmax generalizes the classical
parallel machine scheduling problem with a single server which performs only
the loading (i.e., setup) operation of each job. For this NP-hard problem, no
solution algorithm was proposed in the literature. Therefore, we present two
mixedinteger linear programming (MILP) formulations, one with completion-time
variables along with two valid inequalities and one with time-indexed
variables. In addition, we propose some polynomial-time solvable cases and a
tight theoretical lower bound. In addition, we show that the minimization of
the makespan is equivalent to the minimization of the total idle times on the
machines. To solve large-sized instances of the problem, an efficient General
Variable Neighborhood Search (GVNS) metaheuristic with two mechanisms for
finding an initial solution is designed. The GVNS is evaluated by comparing its
performance with the results provided by the MILPs and another metaheuristic.
The results show that the average percentage deviation from the theoretical
lower-bound of GVNS is within 0.642%. Some managerial insights are presented
and our results are compared with the related literature.Comment: 40 pages, 4 figures, 16 table
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