Hadi Goudarzi

Multi-dimensional SLA-Based Resource Allocation for Multi-tier Cloud Computing Systems

With increasing demand for computing and memory, distributed computing systems have attracted a lot of attention. Resource allocation is one of the most important challenges in the distributed systems specially when the clients have Service Level Agreements (SLAs) and the total profit in the system depends on how the system can meet these SLAs. In this paper, an SLA-based resource allocation problem for multi-tier applications in the cloud computing is considered. An upper bound on the total profit is provided and an algorithm based on force-directed search is proposed to solve the problem. The processing, memory requirement, and communication resources are considered as three dimensions in which optimization is performed. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm.

SLA-based Optimization of Power and Migration Cost in Cloud Computing

Cloud computing systems (or hosting datacenters) have attracted a lot of attention in recent years. Utility computing, reliable data storage, and infrastructure-independent computing are example applications of such systems. Electrical energy cost of a cloud computing system is a strong function of the consolidation and migration techniques used to assign incoming clients to existing servers. Moreover, each client typically has a service level agreement (SLA), which specifies constraints on performance and/or quality of service that it receives from the system. These constraints result in a basic trade-off between the total energy cost and client satisfaction in the system. In this paper, a resource allocation problem is considered that aims to minimize the total energy cost of cloud computing system while meeting the specified client-level SLAs in a probabilistic sense. The cloud computing system pays penalty for the percentage of a clients requests that do not meet a specified upper bound on their service time. An efficient heuristic algorithm based on convex optimization and dynamic programming is presented to solve the aforesaid resource allocation problem. Simulation results demonstrate the effectiveness of the proposed algorithm compared to previous work.

Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System

By utilizing Virtual Machines (VM) and doing server consolidation in a datacenter, a cloud provider can reduce the total energy consumption for servicing his clients with little performance degradation. In particular, the cloud provider can take advantage of dissimilar workloads and by assigning these workloads to the same server, can utilize fewer active servers to service his clients. Placing multiple copies of a VM on different servers and distributing the incoming requests among these VM copies can reduce the resource requirement for each VM copy and help the cloud provider utilize the servers more efficiently. In this paper, the problem of energy-efficient VM placement in a cloud computing system is solved. Precisely, we present an approach that first creates multiple copies of VMs and then uses dynamic programming and local search to place these copies on the physical servers. Simulation results show that the proposed algorithm reduces the total energy consumption by up to 20% with respect to previous work.

Demand-side load scheduling incentivized by dynamic energy prices

Demand response is an important part of the smart grid technologies. This is a particularly interesting problem with the availability of dynamic energy pricing models. Electricity consumers are encouraged to consume electricity more prudently in order to minimize their electric bill, which is in turn calculated based on dynamic energy prices. In this paper, task scheduling policies that help consumers minimize their electrical energy cost by setting the time of use (TOU) of energy in the facility. Moreover, the utility companies can reasonably expect that their customers reduce their consumption at critical times in response to higher energy prices during those times. These policies target two different scenarios: (i) scheduling with a TOU-dependent energy pricing function subject to a constraint on total power consumption; and (ii) scheduling with a TOU and total power consumption-dependent pricing function for electricity consumption. Exact solutions (based on Branch and Bound) are presented for these task scheduling problems. In addition, a rank-based heuristic and a force directed-based heuristic are presented to efficiently solve the aforesaid problems. The proposed heuristic solutions are demonstrated to have very high quality and competitive performance compared to the exact solutions. Moreover, ability of demand shaping utilizing the aforementioned pricing schemes is demonstrated by the simulation results.

Concurrent optimization of consumer's electrical energy bill and producer's power generation cost under a dynamic pricing model

Demand response is a key element of the smart grid technologies. This is a particularly interesting problem with the use of dynamic energy pricing schemes which incentivize electricity consumers to consume electricity more prudently in order to minimize their electric bill. On the other hand optimizing the number and production time of power generation facilities is a key challenge. In this paper, three models are presented for consumers, utility companies, and a third-part arbiter to optimize the cost to the parties individually and in combination. Our models have high quality and exhibit superior performance, by realistic consideration of non-cooperative energy buyers and sellers and getting real-time feedback from their interactions. Simulation results show that the energy consumption distribution becomes very stable during the day utilizing our models, while consumers and utility companies pay lower cost.

Resource allocation and consolidation in a multi-core server cluster using a Markov decision process model

Distributed computing systems have attracted a lot of attention due to increasing demand for high performance computing and storage. Resource allocation is one of the most important challenges in the distributed systems especially when the clients have some Service Level Agreements (SLAs) and the total profit depends on how the system can meet these SLAs. In this paper, an SLA-based resource allocation problem in a server cluster is considered. The objective is to maximize the total profit, which is the total price gained from serving the clients subtracted by the operation cost of the server cluster. The total price depends on the average request response time for each client as defined in their utility functions, while the operating cost is related to the total energy consumption. A joint optimization framework is proposed, comprised of request dispatching, dynamic voltage and frequency scaling (DVFS) for individual cores, as well as server-level and core-level consolidations. Each core in the cluster is modeled using a continuous-time Markov decision process (CTMDP). A near-optimal hierarchical solution is proposed, consisting of a central manager and distributed local agents. Each local agent employs linear programming-based CTMDP solving method to solve the DVFS problem for the corresponding core. The central manager solves the request dispatching problem and finds the optimal number of turned on cores and servers for request processing, thereby achieving a desirable tradeoff between service request response time and power consumption. Experimental results demonstrate that the proposed near-optimal resource allocation and consolidation algorithm consistently outperforms baseline algorithms.

Geographical Load Balancing for Online Service Applications in Distributed Datacenters

This work focuses on the load balancing problem for online service applications (which are response time-sensitive) considering a distributed cloud system comprised of geographically dispersed, heterogeneous datacenters. An offline solution based on force-directed scheduling is presented, which can determine the application placement for long periods of time. The solution is then extended to do online application placement and migration for geographically distributed datacenters based on predictions about the application lifetimes, workload intensities, dynamic energy prices, and renewable energy generation capacities at different datacenters in the cloud system. The simulation results demonstrate 27% to 40% improvement using the proposed algorithms with respect to the method that does not consider the geographical load balancing.

Equal Power Allocation scheme for cooperative diversity

In this paper, we present the Equal power allocation (EPA) algorithm for power allocation and partner selection under a given constraint of outage probability. The proposed algorithm is used for a cooperative diversity system using amplify-and-forward scheme. We represent the problem with a new formulation to find the minimum total required power satisfying the outage probability constraint. We also present a low complexity algorithm for selecting the partner node among all candidate partners. We develop the analytical model and evaluate the results for some typical cases to demonstrate that the performance of the EPA algorithm. It is shown that the proposed algorithm achieves almost the same performance as the previously published algorithms while reducing its implementation complexity.

Robust optimization of a Chip Multiprocessor's performance under power and thermal constraints

Power dissipation and die temperature have become key performance limiters in todays high-performance Chip Multiprocessors (CMPs.) Dynamic power management solutions have been proposed to manage resources in a CMP based on the measured power dissipation, performance, and die temperature of processing cores. In this paper, we develop a robust framework for power and thermal management of heterogeneous CMPs subject to variability and uncertainty in system parameters. More precisely, we first model and formulate the problem of maximizing the task throughput of a heterogeneous CMP (a.k.a., asymmetric multi-core architecture) subject to a total power budget and a per-core temperature limit. Next we develop a solution framework, called Variation-aware Power/Thermal Manager (VPTM), which is a hierarchical dynamic power and thermal management solution targeting heterogeneous CMP architectures. VPTM utilizes dynamic voltage and frequency scaling (DVFS) and core consolidation techniques to control the core power consumptions, which implicitly regulate the core temperatures. An algorithm is proposed for core consolidation and application assignment, and a convex program is formulated and solved to produce optimal DVFS settings. Finally, a feedback controller is employed to compensate for variations in key system parameters at runtime. Experimental results show highly promising performance improvements for VPTM compared to the state-of-the-art techniques.

Optimal partner selection and power allocation for Amplify and Forward cooperative diversity

In this paper, we present a novel algorithm for partner selection and power allocation in the amplify-and-forward cooperative diversity that minimizes the required total transmit power by given outage probability constraint. We represent the problem with new formulation and solve the optimal power allocation by KKT method for a fixed set of partners. For optimal partner selection, we use a novel algorithm with low complexity to find the best set with minimum required power. We present simulation results to demonstrate that the outcomes of the proposed algorithm are very close to results of full search for optimal set.