Deployment and performance evaluation of a SNAP-based resource broker on the White Rose grid

Resource brokering is an essential component in building effective Grid systems. The aim of this paper is to evaluate the performance of a SNAP (Service Negotiation and Acquisition Protocol) based resource broker on a large distributed Grid infrastructure, the White Rose Grid. The broker uses a three-phase commit protocol to reserve resources on demand, as the traditional advance reservation facilities cannot cater for such needs due to the prior time that it requires to schedule reservations. Experiments are designed and carried out on the White Rose Grid. The experimental results show that the inclusion of the three-phase commit protocol provides a performance enhancement on a large distributed Grid Infrastructure, in terms of the time taken from submission of user requirements until a job begins execution. The results support those previously obtained through the use of mathematical modelling and simulation. The broker is a viable contender for use in future Grid resource brokering implementations

Rapid and accurate energy models through calibration with IPMI and RAPL

Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models. IPMI and RAPL are subject to error and partial measurement, which may be mitigated. Models allow for prediction and more responsive measures of power consumption, but require calibrating. The causes of calibration error are discussed, along with mitigation strategies, without overly complicating the underlying model. An outcome is a Watt meter emulator that provides hosts level power measurement along with estimated power consumption for a given workload, with an average error of 0.20W

Energy Consumption-based Pricing Model for Cloud Computing

Pricing mechanisms employed by di erent service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how di erent pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace

Enabling service-level agreement renegotiation through extending WS-Agreement specification

WS-Agreement is a language and protocol designed for creating service-level agreements (SLAs) based on initial offers, and for monitoring those offers at runtime. The definition of WS-Agreement protocol is very general and does not contemplate the possibility of changing an agreement at runtime. This paper presents extensions of the WS-Agreement specification to support the dynamic nature of SLAs by allowing the possibility of SLA renegotiation at runtime. The extended WS-Agreement specification has been implemented and tested. Within this implementation, the concept of renegotiation is demonstrated through the ability to create more than one SLA at runtime. An evaluation is conducted to examine the profits a service provider may gain through renegotiation, as well the savings resulting from rescuing the SLA from violations as a consequence of avoiding paying penalties. The results show that making the SLA terms adaptable and changeable is a viable mechanism that provides flexibility to the service provider and service consumer

A Graph-Based Approach to Address Trust and Reputation in Ubiquitous Networks

The increasing popularity of virtual computing environments such as Cloud and Grid computing is helping to drive the realization of ubiquitous and pervasive computing. However, as computing becomes more entrenched in everyday life, the concepts of trust and risk become increasingly important. In this paper, we propose a new graph-based theoretical approach to address trust and reputation in complex ubiquitous networks. We formulate trust as a function of quality of a task and time required to authenticate agent-to-agent relationship based on the Zero-Common Knowledge (ZCK) authentication scheme. This initial representation applies a graph theory concept, accompanied by a mathematical formulation of trust metrics. The approach we propose increases awareness and trustworthiness to agents based on the values estimated for each requested task, we conclude by stating our plans for future work in this area

Introducing risk management into the grid

Service Level Agreements (SLAs) are explicit statements about all expectations and obligations in the business partnership between customers and providers. They have been introduced in Grid computing to overcome the best effort approach, making the Grid more interesting for commercial applications. However, decisions on negotiation and system management still rely on static approaches, not reflecting the risk linked with decisions. The EC-funded project "AssessGrid" aims at introducing risk assessment and management as a novel decision paradigm into Grid computing. This paper gives a general motivation for risk management and presents the envisaged architecture of a "risk-aware" Grid middleware and Grid fabric, highlighting its functionality by means of three showcase scenarios

Accuracy of Energy Model Calibration with IPMI

Energy consumption in Cloud computing is a significant issue and affects aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Monitoring and prediction provides the groundwork for improving the energy efficiency of data centers. This monitoring however is required to be fast and efficient without unnecessary overhead. It is also required to scale to the size of a data center where measurement through directly attached Watt meters is unrealistic. This therefore requires models that translate resource utilisation into the power consumed by a physical host. These models require calibrating and are hence subject to error. We discuss the causes of error within these models, focusing upon the use of IPMI in order to gather this data. We make recommendations on ways to mitigate this error without overly complicating the underlying model. The final result of these models is a Watt meter emulator that can provide values for power consumption from hosts in the data center, with an average error of 0.20W

Solving MTU Mismatch and Broadcast Overhead of NDN over Link-layer Networks

Named Data Networking (NDN) has been considered as a promising Internet architecture for the future data-centric communication. In particular, NDN over link-layer networks would cut off the overheads of Transmission Control Protocol/Internet Protocol (TCP/IP), and enhance the efficiency of data distribution. However, there are two main unsolved issues for the NDN link-layer, namely broadcast overhead and Maximum Transmission Unit (MTU) mismatch. In this paper, we have therefore designed and implemented an NDN Neighborhood Discovery Protocol, named NDN-NDP, to enable a unicast data transmission over the link-layer. Furthermore, our NDN-NDP has included a negotiation mechanism to fix the MTU mismatch issue. In comparison to previously proposed NDN link-layer technologies, we can fix both MTU mismatch and broadcast overhead problems. Through emulation and experiments on a test-bed, we have also compared our NDN-NDP with the Link-layer Protocol for NDN (NDNLP), which is the most widely deployed NDNLP. From our experiments, NDN-NDP can efficiently fix MTU mismatch and broadcast overhead issue

Fuzzy logic based qos optimization mechanism for service composition

Increase emphasis on Quality of Service and highly changing environments make management of composite services a time consuming and complicated task. Adaptation approaches aim to mitigate the management problem by adjusting composite services to the environment conditions, maintaining functional and quality levels, and reducing human intervention. This paper presents an adaptation approach that implements self-optimization based on fuzzy logic. The proposed optimization model performs service selection based on the analysis of historical and real QoS data, gathered at different stages during the execution of composite services. The use of fuzzy inference systems enables the evaluation of the measured QoS values, helps deciding whether adaptation is needed or not, and how to perform service selection. Experimental results show significant improvements in the global QoS of the use case scenario, providing reductions up to 20.5% in response time, 33.4% in cost and 31.2% in energy consumption

A methodology correlating code optimizations with data memory accesses, execution time and energy consumption

The advent of data proliferation and electronic devices gets low execution time and energy consumption software in the spotlight. The key to optimizing software is the correct choice, order as well as parameters of optimization transformations that has remained an open problem in compilation research for decades for various reasons. First, most of the transformations are interdependent and thus addressing them separately is not effective. Second, it is very hard to couple the transformation parameters to the processor architecture (e.g., cache size) and algorithm characteristics (e.g., data reuse); therefore, compiler designers and researchers either do not take them into account at all or do it partly. Third, the exploration space, i.e., the set of all optimization configurations that have to be explored, is huge and thus searching is impractical. In this paper, the above problems are addressed for data-dominant affine loop kernels, delivering significant contributions. A novel methodology is presented reducing the exploration space of six code optimizations by many orders of magnitude. The objective can be execution time (ET), energy consumption (E) or the number of L1, L2 and main memory accesses. The exploration space is reduced in two phases: firstly, by applying a novel register blocking algorithm and a novel loop tiling algorithm and secondly, by computing the maximum and minimum ET/E values for each optimization set. The proposed methodology has been evaluated for both embedded and general-purpose CPUs and for seven well-known algorithms, achieving high memory access, speedup and energy consumption gain values (from 1.17 up to 40) over gcc compiler, hand-written optimized code and Polly. The exploration space from which the near-optimum parameters are selected is reduced from 17 up to 30 orders of magnitude