Ionut Anghel

Energy Aware Dynamic Resource Consolidation Algorithm for Virtualized Service Centers Based on Reinforcement Learning

In this paper we propose an energy aware dynamic consolidation algorithm for virtualized service centers based on reinforcement learning. The energy awareness is enacted by using the Energy Aware Context Model (EACM) to programmatically represent the current service center context situation by means of ontologies. We have defined the EACM model entropy metric for evaluating the service center greenness level. If the entropy value is above a predefined threshold, the service center is not in a green state. As a consequence, consolidation or dynamic power management actions are selected by means of reinforcement learning and executed to bring back the service center in an energy efficient state. The results are promising showing that the proposed energy aware consolidation algorithm decreases the energy consumption with about 26% from the total energy consumption of a service center.

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

RAP - a basic context awareness model

This paper addresses two fundamental research problems in the domain of context sensitive systems: the development of a generic context model that can be used to represent general purpose contexts in a computer interpretable way and the context model management. The context model is represented using a triple set consisting of context resources, actors and policies. The model is mapped onto real contexts by populating the sets with context specific elements. A context situation to which a context aware system must adapt is represented by a specific context model instance. To ease the context reasoning and adaptation processes, a core ontology is defined to represent the relationships between the context model concepts. The core ontology is extended with domain specific concepts as ontology sub-trees. For the context model management problem we propose an agent based solution using BDI agents.

Cloud SLA negotiation for energy saving — A particle swarm optimization approach

The necessity of balancing the obtained performance with the energy consumed is an emerging ambition for cloud computing research. Performance in cloud computing is defined through Service Level Agreement contracts between the cloud provider and cloud customer, being a projection of the customers perspective on the service offered by the cloud provider. Although more and more research efforts go into standardizing Service Level Agreement in cloud systems, the area is still at its early ages. This paper proposes a Service Level Agreement negotiation protocol based on particle swarm optimization techniques, for obtaining a balance between the energy consumed and performance offered in the cloud. The two parties of the defined negotiation protocol are the performance-oriented cloud customer and the energy-oriented cloud provider. The agreement resulted from the negotiation process satisfies the two major negotiation properties we aim for: closeness to Pareto optimality and high social welfare.

Setting energy efficiency goals in data centers: the GAMES approach

Energy-aware service centers take into account energy consumption of infrastructures, machines, applications, storage systems, and their distributed computing architecture. The approach to energy efficiency in data centers in the GAMES (Green Active Management of Energy in IT Service centers) project is presented: Green Performance Indicators (GPIs), i.e., properties that, continuously monitored, evidence the level of consumed energy by the centers IT resources, can be the basis of a systematic approach to increase energy efficiency. The GPIs are the basis for improving energy efficiency with adaptive actions and to achieve a higher level of green maturity, as prescribed, for instance, in the GreenGrid Data Center Maturity Model (DCMM), based on a usage-centric perspective in GPIs. The paper briefly describes monitoring of GPIs and the adaptation actions adopted to reach the green goals. Preliminary experimental results are discussed.

A swarm-inspired data center consolidation methodology

This paper proposes a swarm-inspired data center consolidation methodology which aims at reducing the power consumption in data centers while ensuring the workload execution within the pre-established performance parameters. Each data center server is managed by an intelligent agent that deals with its power efficiency by implementing a birds migration-inspired behavior to decide on the appropriate server consolidation actions. The selected actions are executed to achieve an optimal utilization of server computing resources thus lowering power consumption. The data center servers self-organize in logical clusters according to the birds V-formation self-organizing migration model. The results are promising showing that the swarm-inspired data center consolidation methodology optimizes the utilization ratio of the data center computing resources and achieves estimated power savings of about 16%.

A Policy-Based Context Aware Self-Management Model

This paper proposes a generic policy based self-management model that can be used to automatically detect and repair the problems appeared during the context adaptation processes. To successfully capture and evaluate the dynamic rules that govern the context aware adaptation processes we have defined an generic context policy representation model and its associated reasoning language conversion model for run-time evaluation. To evaluate the run-time degree of respecting the context policies we define and formalize the concept of context entropy. The context information is modeled in a system programmatic manner using both the set based and ontology based representations provided by our RAP (Resources, Actors, Policies) context model. The context model artifacts are generated and administrated at run time by a management infrastructure based on BDI (Believe, Desire, Intentions) agents. The model was tested and validated within the premises of our Distributed Systems Research Laboratory smart environment.

Optimized flexibility management enacting Data Centres participation in Smart Demand Response programs

Abstract In this paper we address the problem of Data Centres (DCs) integration into the Smart Grid scenario by proposing a technique for scheduling and optimizing their operation allowing them to participate in Smart Demand Response programs. The technique is leveraging on DCs available flexible energy resources, on mechanisms for eliciting this latent flexibility and on an innovative electronic marketplace designed for trading energy flexibility and ancillary services. This will enact DCs to shape their energy demand to buy additional energy when prices are low and sell energy surplus when prices are high. At the same time DCs will be able to provide increased energy demand due to a large un-forecasted renewable energy production in their local grid, shed or shift energy demand over time to avoid a coincidental peak load, provide fast ramping power by turning on their backup fossil fuelled generators and injecting the energy surplus in the grid and finally provide reactive power regulation by changing their power factor. Numerical simulations results considering traces of an operational DC indicate the great potential of the proposed technique for supporting DCs participation in Smart Demand Response programs.

A Layered Workflow Model Enhanced with Process Algebra Verification for Industrial Processes

This paper addresses the problem of modeling and verification of complex industrial production lines that include physical machines. The paper proposes a methodology for building business models, organized on layers of increasing complexity, from production line elementary machines and sensors to complex business workflows. For workflow verification purposes, the model is represented in Process Algebra (PA) formalism which allows for reasoning and checking the correctness ofthe business process model and early identification of any logical faults in the model design phase. The resulted model, validated using with the PA formalism can be translated, deployed and executed by any workflow execution engine. The proposed methodology was used for modeling and verifying a sausage processing line, developed in the context of the Food Trace [4] research project.

Data center optimization methodology to maximize the usage of locally produced renewable energy

In this paper we address the problem of Data Centers energy efficiency by proposing a methodology which aims at planning the Data Center operation such that the usage of locally produced renewable energy is maximized. We defined a flexibility mechanism and model for Data Centers components (electrical cooling system, IT workload, energy storage and diesel generators) leveraging on optimization actions such as load time shifting, alternative usage of non-electrical cooling devices such as the thermal storage or charging/discharging the electrical storage devices, etc. The flexibility mechanism enacts the possibility of shifting the Data Centers energy demand profile from time intervals with limited renewable energy production due to weather conditions, to time intervals when spikes of renewable energy production are predicted. We have developed a simulation environment which allows the methodology to be inlab tested and evaluated. Results are promising showing an increase of renewable energy usage of 12% due to energy consumption demand shift for following the renewable energy production levels.