Transaction Scheduling Research Articles

Analysis of The Effectiveness of the BukuWarung Application as a Financial Management Tool for MSMEs

Effective financial management is crucial for business success, especially amid the Industry 5.0 revolution, which has transformed production, management, and human-machine interactions. This era has introduced advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), robotics, and additive manufacturing into production environments, leading to significant changes in accounting practices. In Indonesia, SMEs, traditionally reliant on manual record-keeping, are encouraged to adopt digital applications to remain current. Consequently, various financial management applications have emerged, including BukuWarung, designed to streamline transaction recording and enhance financial management efficiency. This research aims to assess the effectiveness of BukuWarung as a financial management tool for SMEs. Utilizing a qualitative and descriptive approach, the study involved SMEs at SWK Lidah Wetan that use BukuWarung. Primary data were gathered through observation, interviews, and field documentation. Findings reveal that BukuWarung significantly aids SME financial management, particularly in billing, bookkeeping, cashier operations, inventory management, and transaction scheduling. Future research should explore the utilization of specific features within BukuWarung and their effects on SME financial management.

Towards Optimal Transaction Scheduling

Maximizing transaction throughput is key to high-performance database systems, which focus on minimizing data access conflicts to improve performance. However, finding efficient schedules that reduce conflicts remains an open problem. For efficiency, previous scheduling techniques consider only a small subset of possible schedules. In this work, we propose systematically exploring the entire schedule space, proactively identifying efficient schedules, and executing them precisely during execution to improve throughput. We introduce a greedy scheduling policy, SMF, that efficiently finds fast schedules and outperforms state-of-the-art search techniques. To realize the benefits of these schedules in practice, we develop a schedule-first concurrency control protocol, MVSchedO, that enforces fine-grained operation orders. We implement both in our system R-SMF, a modified version of RocksDB, to achieve up to a 3.9× increase in throughput and 3.2× reduction in tail latency on a range of benchmarks and real-world workloads.

Scenario-oriented multi-area joint market clearing of energy and reserve under uncertainty

The increasing uncertain renewable generations throughout the entire interconnection call for a tighter coordination of different local system operators (SOs) beyond their independent operations. In this paper, the multi-area joint market clearing of energy and reserve is studied on the basis of Generalized Coordinated Transaction Scheduling (GCTS), which can achieve the optimal dispatch asymptotically with interface bids from market participants. In the proposed model, possible outages and load/renewable forecast errors in the interconnection are modeled as several non-base scenarios in addition to the nominal operation, while nominal energy interchange and reserve coordination for possible non-base scenarios between different areas are endogenously scheduled by procuring energy/reserve interface bids between their boundary buses, leading to the minimized expected market cost among all areas. Based on the proposed model, marginal prices of internal energy, reserve, and external energy/reserve interface bids are derived. Using these prices, internal resources are settled by the corresponding local SO, while external interface bids are jointly settled by all SOs and the tie-line operator (TO). We prove that in expectation, the merchandise surplus of (i) any local SO and (ii) the TO is equal to the congestion rent of (i) internal lines and (ii) tie-lines, respectively. Meanwhile, incentive compatibilities of market participants are also guaranteed. These findings are validated in numerical simulations.

QoS-pro: A QoS-enhanced Transaction Processing Framework for Shared SSDs

Solid State Drives (SSDs) are widely used in data-intensive scenarios due to their high performance and decreasing cost. However, in shared environments, concurrent workloads can interfere with each other, leading to a violation of Quality of Service (QoS). While QoS mechanisms like fairness guarantees and latency constraints have been integrated into SSDs, existing transaction processing frameworks offer limited QoS guarantees and can significantly degrade overall performance in a shared environment. The reason is that the internal components of an SSD, originally designed to exploit parallelism, struggle to coordinate effectively when QoS mechanisms are applied to them. This article proposes a novel QoS -enhanced transaction pro cessing framework, called QoS-pro, which enhances QoS guarantees for concurrent workloads while maintaining high parallelism for SSDs. QoS-pro achieves this by redesigning transaction processing procedures to fully exploit the parallelism of shared SSDs and enhancing QoS-oriented transaction translation and scheduling with parallelism features in mind. In terms of fairness guarantees, QoS-pro outperforms state-of-the-art methods by achieving 96% fairness improvement and 64% maximum latency reduction. QoS-pro also shows almost no loss in throughput when compared with parallelism-oriented methods. Additionally, QoS-pro triggers the fewest Garbage Collection (GC) operations and minimally affects concurrently running workloads during GC operations.

Voltage Stability Assessment of a Campus DC Microgrid Implemented in Korea as a Blockchain-Based Power Transaction Testbed

Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM.

Transaction Scheduling: From Conflicts to Runtime Conflicts

This paper studies how to improve the performance of main memory multicore OLTP systems for executing transactions with conflicts. A promising approach is to partition transaction workloads into mutually conflict-free clusters, and distribute the clusters to different cores for concurrent execution. We show that if transactions in each cluster are properly scheduled, transactions that are traditionally considered conflicting can be executed without conflicts at runtime. In light of this, we propose to schedule transactions and reduce runtime conflicts, instead of partitioning based on the conventional notion of conflicts. We formulate the transaction scheduling problem to minimize runtime conflicts, and show that the problem is NP-complete. This said, we develop an efficient scheduling algorithm to improve parallelism. Moreover, for transactions that are not packed in batches, we show that runtime conflict analysis also helps reduce conflict penalties, by proposing a proactive deferring method. Using standard and enhanced benchmarks, we show that on average our scheduling and proactive deferring methods improve the throughput of existing partitioners and concurrency control protocols by 131% and 109%, respectively, up to 294% and 152%.

Opportunities for Quantum Acceleration of Databases: Optimization of Queries and Transaction Schedules

The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.

Coordinated Transaction Scheduling in Multi-Area Electricity Markets: Equilibrium and Learning

Tie-line scheduling in multi-area power systems in the United States largely proceeds through a market-based mechanism called Coordinated Transaction Scheduling (CTS). We analyze this market mechanism through a game-theoretic lens. Our analysis characterizes the effect of market liquidity, market participants’ forecasts about inter-area price spreads, transactions fees and coupling of CTS markets with up-to-congestion virtual transactions. Using real data, we empirically verify that the market outcomes with CTS bidders, employing simple learning algorithms, support the conclusions from our equilibrium analysis of the games.

Complete formal verification of the PSTM transaction Scheduler

State of the art formal verification is based on formal methods and its goal is proving given correctness properties. For example, a PSTM scheduler was modeled in CSP in order to prove deadlock-freeness and starvation-freeness. However, as this paper shows, using solely formal methods is not sufficient. Therefore, in this paper we propose a complete formal verification of trustworthy software, which jointly uses formal verification and formal model testing. As an example, we first test the previous CSP model of PSTM transaction scheduler by comparing the model checker PAT results with the manually derived expected results, for the given test workloads. Next, according to the results of this testing, we correct and extend the CSP model. Finally, using PAT results for the new CSP model, we analyze the performance of the PSTM online transaction scheduling algorithms from the perspective of the relative speedup.

Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel Networks

Payment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment.

Fundamental Studies of Smart Distributed Energy Resources along with Energy Blockchain

This article studies the broad methodology and major application of smart distributed energy resources (DER) in terms of energy generation, consumption, transaction, and power scheduling. This article simplifies a general DER system into a generic type of integrated DER model. This model is used to investigate a smart DER system that transforms three input parameters, (3I parameters) into three critical output functions(3O functions); hence, the model is also called the 3I3O model. The power at a common connection joint can be enabled by a computer that makes computerized decisions to utilize smart DER. Therefore, the computer algorithm collects various data fed into a computer for deep learning and artificial intelligence (AI) decision making. The authors demonstrate important results and the best solutions to meet power demand, offer an economic advantage and have a low carbon footprint for consumers. Moreover, several network blockchain options are discussed. EBC and DER represent an ideal combination with advantages in managing exergy through so-called intelligent power technology. This technology is discussed in detail and includes special hardware, software, and a broad set of computerized intelligence. Finally, the exergy that can possibly be achieved for smart DER systems is discussed.

A reinforcement learning approach for transaction scheduling in a shuttle‐based storage and retrieval system

AbstractWith recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle‐based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini‐loads. With the recent increase in e‐commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh‐speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e.,Q‐learning) approach on a newly proposed tier‐to‐tier SBS/RS design, redesigned from a traditional tier‐captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML‐based algorithm for transaction selection in that system. The ML‐based solution is compared with traditional scheduling approaches: first‐in‐first‐out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, theQ‐learning approach performs better than the two static scheduling approaches.

Research on self-balancing transaction scheduling strategy of new energy power in County considering section load rate

Under the dual carbon target, with the gradual increase of the scale of new energy grid connection, the randomness and volatility of new energy output will further affect the safe and stable operation of the system. In view of the frequent fluctuation of the section load rate of the county external liaison line, this paper proposes a self balancing transaction scheduling strategy of new energy power within the county considering the section load rate, and establishes a day ahead real-time two-tier transaction joint optimization model considering the section load rate. The simulation calculation shows that this strategy can significantly improve the local consumption rate of new energy compared with the simple physical method using only energy storage technology, Reduce the power flow fluctuation of tie line, optimize the section load rate and maintain the stability of power grid.

Joint Covering Congestion Rents in Multi-area Power Systems Considering Loop Flow Effects

We consider the problem of how multiple areas should jointly cover congestion rents of internal and tie-lines in an interconnected power system. A key issue of our concern is the loop flow problem, which represents discrepancies between scheduled and actual power flow distributions because electric power does not always flow along the most direct paths of transactions. We employ generalized coordinated transaction scheduling (GCTS) for interchange scheduling, which can eliminate dispatch errors caused by loop flow effects and asymptotically converge to the joint economic dispatch (JED) under ideal assumptions. Subsequently, distributed algorithms are proposed for each area to recover multipliers of the global GCTS model, as well as quantifying and pricing its contribution to line congestions. Thereby, all areas and interface bids can jointly cover congestion rents of internal and tie-lines with their merchandise surpluses and profits. Simulations demonstrate the effectiveness of the proposed approach of LMP recovery and joint covering congestion rents.

Database isolation by scheduling

Transaction isolation is conventionally achieved by restricting access to the physical items in a database. To maximize performance, isolation functionality is often packaged with recovery, I/O, and data access methods in a monolithic transactional storage manager. While this design has historically afforded high performance in online transaction processing systems, industry trends indicate a growing need for a new approach in which intertwined components of the transactional storage manager are disaggregated into modular services. This paper presents a new method to modularize the isolation component. Our work builds on predicate locking, an isolation mechanism that enables this modularization by locking logical rather than physical items in a database. Predicate locking is rarely used as the core isolation mechanism because of its high theoretical complexity and perceived overhead. However, we show that this overhead can be substantially reduced in practice by optimizing for common predicate structures. We present DIBS, a transaction scheduler that employs our predicate locking optimizations to guarantee isolation as a modular service. We evaluate the performance of DIBS as the sole isolation mechanism in a data processing system. In this setting, DIBS scales up to 10.5 million transactions per second on a TATP workload. We also explore how DIBS can be applied to existing database systems to increase transaction throughput. DIBS reduces per-transaction file system writes by 90% on TATP in SQLite, resulting in a 3X improvement in throughput. Finally, DIBS reduces row contention on YCSB in MySQL, providing serializable isolation with a 1.4X improvement in throughput.

A Survey On Securing Internet Of Medical Things Using Blockchain

: In this project, we provide a solution to protect and secure the patient records and data against various attacks using the technology of blockchain. We use the fully decentralized blockchain. Also, digital signatures are used for model updates. Therefore, we hold that adversaries are not able to fabricate digital signatures or take control of the majority of the network. Furthermore, an adversary cannot poison the data because it is stored off-chain rather on the public ledger. There are only pointers information encrypted with a hash function inside a public ledger. In problems of large-grid-level centralized transactions and dispatch centers with information asymmetry and high processing costs, the proposal of a completely decentralized transaction architecture and a weak centralized scheduling strategy based on block-chain is done. Firstly, the defined concepts of transaction decentralization and scheduling decentralization are given, and the reliability of distributed transaction communication is studied. Based on the communication credit consensus mechanism, built a blockchain transaction risk control model. Secondly, security checks are performed under the weakly centralized scheduling architecture based on the autonomous chain of substations, and temporary central nodes are set up to perform scheduling tasks. Finally, an optimal solution is obtained by dynamically updating the credibility by using an improved evolutionary algorithm to solve the above model.

Efficient Concurrent Execution of Smart Contracts in Blockchain Sharding

Throughput performance is a critical issue in blockchain technology, especially in blockchain sharding systems. Although sharding proposals can improve transaction throughput by parallel processing, the essence of each shard is still a small blockchain. Using serial execution of smart contract transactions, performance has not significantly improved, and there is still room for improvement. A smart contract concurrent execution strategy based on concurrency degree optimization is proposed for performance optimization within a single shard. This strategy is applied to each shard. First, it characterizes the conflicting contract feature information by executing a smart contract, analyzing the factors that affect the concurrent execution of the smart contracts, and clustering the contract transaction. Second, in shards with high transaction frequency, considering the execution time, conflict rate, and available resources of contract transactions, finding a serializable schedule of contract transactions by redundant computation and a Variable Shadow Speculative Concurrency Control (SCC-VS) algorithm for smart contract scheduling is proposed. Finally, experimental results show that the strategy increases the concurrency of smart contract execution by 39% on average and the transaction throughput of the whole system by 21% on average.

Correction to “Research on Block-Chain-Based Intelligent Transaction and Collaborative Scheduling Strategies for Large Grid”

Correction to “Research on Block-Chain-Based Intelligent Transaction and Collaborative Scheduling Strategies for Large Grid”

The Logical Timestamp Skew Anomaly in Event-Replicated Transaction Schedulers

To sidestep reasoning about the complex effects of concurrent execution, many system designers have conveniently embraced strict serializability on the strength of its claims, support from commercial and open-source database communities and ubiquitous levels of industry adoption. Crucially, distributed components are built on this model; multiple schedulers are composed in an event-driven architecture to form larger, ostensibly correct systems. This paper examines the oft-misconstrued position of strict serializability as a composable correctness criterion in the design of such systems. An anomaly is presented wherein a strict serializable scheduler in one system produces a history that cannot be serially applied to even a weak prefix-consistent replica in logical timestamp order. Several solutions are presented under varying isolation properties, including novel isolation properties contributed by this paper. It is further shown that every nondeterministic scheduler is anomaly-prone, every nonconcurrent scheduler is anomaly-free, and at least one deterministic concurrent scheduler is anomaly-free.

Priority Heuristic in MDRTDBS

Background: Mobile distributed real time database is an emerging field of research. Transaction execute in these system should commit before elapsing the deadline. Assigning priority to arrived and executing transaction is the main function of transaction scheduler. Perform correct order of execution enhance the system performance in time constraint environment. In recent years a number of heuristic policies in DRTDBS are developed to enhance the quality estimation of scheduler. However due to various wireless limitations a very little work is investigated in the MDRTDBS. In this paper we have develop two heuristic policies and perform simulation using pessimistic and optimistic concurrency control algorithm to enhance the system performance. Objective: Two heuristic policies based on write only locks and number of same data item existence has been introduced. These heuristic approaches enhance the performance of MDRTDBS scheduler. Methods: Develop two heuristic policies based on data contention which cope with CC policies like DHP-2PL and FBOCC. These policies count the number of write only locks and existence of number of same data item. Ultimately these policies enhance the system performance. Results:: Investigated the performance using simulator which is designed using C language. Based on these simulation results using pessimistic and optimistic CCs different graphs are plotted which shows the performance of system for these developed heuristic approaches. During simulation the low and high workload of transaction arrival is taken to fair investigate of these heuristic approaches. Conclusion: Simulation results show that the system performance is improved using these heuristic approaches. However, NWL_Modified heuristic approach outperform in MDRTDBS than earlier heuristic approaches.