ABSTRACT Introduction Existing serverless workflow orchestration systems are predominantly designed for a single‐cloud FaaS system, leading to vendor lock‐in. This dependency restricts performance optimization, cost reduction, and the overall availability of applications. However, orchestrating serverless workflows on Jointcloud FaaS systems faces two main challenges: (1) additional overhead caused by centralized cross‐cloud orchestration; and (2) a lack of reliable failover and fault‐tolerant mechanisms for cross‐cloud serverless workflows. Methods To address these challenges, we propose Joint λ , a distributed runtime system designed to orchestrate serverless workflows on multiple FaaS systems without relying on a centralized orchestrator. Joint λ introduces a compatibility layer, Backend‐Shim, which leverages inter‐cloud heterogeneity to optimize makespan and reduce costs with on‐demand billing. By using function‐side orchestration instead of centralized nodes, it enables independent function invocations and data transfers, thereby minimizing cross‐cloud communication overhead. For high availability, it ensures exactly‐once execution via datastores and failover mechanisms for serverless workflows on Jointcloud FaaS systems. Results We validate Joint λ on two heterogeneous FaaS systems, AWS and Aliyun, using four representative workflows. Compared to the most advanced commercial orchestration services for single‐cloud serverless workflows, Joint λ reduces makespan by up to 3.3× while saving up to 65% in cost. Furthermore, Joint λ is up to 4.0× faster than state‐of‐the‐art orchestrators for cross‐cloud serverless workflows, while achieving competitive cost performance in representative scenarios. Conclusions The evaluation demonstrates that Joint λ effectively eliminates vendor lock‐in and mitigates cross‐cloud communication overhead without sacrificing economic efficiency. By incorporating decentralized function‐side orchestration and robust failover mechanisms, it provides strong execution guarantees and high availability for complex serverless workflows across heterogeneous Jointcloud FaaS environments.

Large language models (LLMs) enable versatile applications, with fine-tuning methods like LoRA optimizing them for specific domains. However, ensuring QoS for dynamic LoRA inference on heterogeneous devices is challenging due to memory contention from the shared LLM base model and KV cache. Existing solutions lack coordination between the KV cache and LoRA scheduling. We propose JointSerLoRA, a two-level inference system featuring: (1) a disaggregated architecture for KV cache consistency, (2) a global scheduler balancing adaptercache reuse and GPU load, and (3) a local scheduler with adaptive batching for prefix and adapter-aware grouping. Evaluations show that JointSerLoRA reduces end-to-end latency by 29 % and TTFT by$3.8 \times$compared to state-of-the-art systems.

With the expanding scale of current industries, monitoring systems centered around Key Performance Indicators (KPIs) play an increasingly crucial role. KPI anomaly detection can monitor the potential risks according to KPI data and has garnered widespread attention due to its rapid responsiveness and adaptability to dynamic changes. Considering the absence of labels and the high cost of manual annotation of KPI data, the self-supervised approaches are proposed. Among them, mask modeling methods draw great attention and can learn the intrinsic distribution of data without relying on prior assumptions. However, conventional mask modeling often overlooks the examination of relationships between unsynchronized variables, treating them with equal importance, and inducing inaccurate detection results. To address this, this paper proposes a Dual Masked modeling Approach combined with Similarity Aggregation, named DMASA. Starting from a self-supervised approach based on mask modeling, DMASA incorporates spectral residual techniques to explore inter-variable dependencies and aggregates information from similar data to eliminate interference from irrelevant variables in anomaly detection. Extensive experiments on eight datasets and state-of-the-art results demonstrate the effectiveness of our approach. Our code is available athttps://github.com/colaudiolab/GT-DMASA.

LLM-based workflows utilize Large Language Models (LLMs) for dynamic user requests, combining task planning and multiple machine learning (ML) models. Existing ML workflow platforms assume static structure deployment, neglecting dynamic orchestration for comprehensive workflows that fulfill users’ diverse requirements. Model selection in dynamic workflows involves various models and parallelism configurations, each with unique accuracy and efficiency trade-offs.To address these limitations, we introduce JointFlow, a solution offering LLM-based workflows as a service by dynamically constructing workflows across heterogeneous JointCloud infrastructures. It models and optimizes dynamic workflows, focusing on accuracy and efficiency trade-offs in model selection and parallelism configurations. A super-DAG represents the dynamic sub-task workflows from LLMs, profiling configurations across infrastructures. JointFlow also seeks the optimal workflow placement strategy. Experiments show it reduces serving costs while achieving throughput objectives compared to state-of-the-art methods.

Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution.

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud provider.To address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between them.In this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

In blockchain, there has long been a contradiction between the limited ability and the uncertain requirements of processing transactions, which seriously restricts the practical application of blockchain. Therefore, how to improve the scalability of blockchain has become an urgent issue to be solved. Some existing works have achieved blockchain expansion through increasing the upper limit of block size permanently, which makes the trade-off of the “Mundellian Trilemma ” in blockchain (i.e. a blockchain system cannot be optimal in all the three dimensions of scalability, security and decentralization at the same time) fixed and thus not adapted to the dynamic environment. In this paper, we propose FSS, a flexible scaling scheme for blockchain based on stale block rate, which dynamically adjusts the upper limit of block size according to the stale block rate, not only expanding the blockchain when allowed, but also shrinking it when necessary. Experimental results indicate that FSS can reasonably improve the scalability of blockchain with required stale block rate.

For reinforcement learning, the extrinsic reward is a core factor for the learning process which however can be very sparse or completely missing. In response, researchers have proposed the idea of intrinsic reward, such as encouraging the agent to visit novel states through prediction error. However, the deep prediction model can provide over-confident and miscalibrated predictions. To mitigate the impact of inaccurate prediction, previous research applied deep ensembles and achieved superior results, despite the increased computation and storage space. In this paper, inspired by the uncertainty estimation, we leverage Monte Carlo Dropout to generate intrinsic reward from the perspective of uncertainty estimation with the goal to decrease the demands for computing resources while retaining superior performance. Utilizing the simple yet effective approach, we conduct extensive experiments across a variety of benchmark environments. The experimental results suggest that our method provides a competitive performance in final score and is faster in running speed, while requiring much fewer computing resources and storage space.

As the new paradigm of JointCloud Computing matures, enterprises are trying to build multiple Kubernetes clusters on different clouds to deploy tasks, with the advantages of disaster backup, low latency, and avoidance of single vendor lock-in, etc. Tasks in a JointCloud environment, always have highly diversified resource demands on CPU, memory, disk, and network. However, the mismatch between these tasks and heterogeneous clusters can easily cause many resource fragments, resulting in low resource availability. Therefore, the task scheduling strategy is the key to solving the above problem. The existing task schedule strategies for multi-clusters are always aiming at clusters’ load balancing instead of increasing the resource availability. In this paper, we propose a dynamic task scheduling framework with the design of multi-cluster resource high-availability schedule strategy (MRASS) based on historical task resource consumption. MRASS conducts a cooperation model between multiple clusters and tasks, and proposes an indicator of resource availability, which is used to optimize the proportion of remaining resources of the cluster to keep approaching the proportion of resource requirements of future tasks, thereby execute more tasks within limited resources. Extensive numerical results confirm that the strategy has stable performance and performs well with different initial cluster resource setting, task resource type and task number. Compared with the existing algorithm, MRASS can place up to 20% more tasks, and the success rate of first placement of tasks can reach over 98%.

Improving the scalability of a multi-agent system is one of the key challenges for applying reinforcement learning to learn an effective policy. Parameter sharing is a common approach used to improve the efficiency of learning by reducing the volume of policy network parameters that need to be updated. However, sharing parameters also reduces the variance between agents’ policies, which further restricts the diversity of their behaviors. In this paper, we introduce a policy parameter sharing approach, it maintains a policy network for each agent, and only updates one of them. The differentiated behavior of agents is maintained by the policy, while sharing parameters are updated through a soft way. Experiments in foraging scenarios demonstrate that our method can effectively improve the performance and also the scalability of the multi-agent systems.

Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical constraints that limit the large-scale application of blockchain: scalability, security, and decentralization cannot be achieved altogether. Consensus algorithm is the core of blockchain, which determines the performance of blockchain system to a certain extent. The existing reviews or surveys mainly focus on processes of consensus algorithms, but fall short in covering the current trends and scenarios, thereby lacking intrinsic understanding of their design philosophy. In this paper, we propose a multi-dimensional tradeoff model and unearth various indicators of different dimensions to guide the construction of consensus algorithms. To summarize the existing efforts, we compare and analyze various classical consensus algorithms, and focus on the design principles of these algorithms under the multi-dimensional tradeoff model. According to different requirements, each algorithm has different tradeoffs. Furthermore, we provide different solutions for blockchain in different dimensions. Finally, we summarize the development trend of consensus design and the key technology prospects of blockchain. This is, to the best of our knowledge, the first survey that accomplishes such goals.

With the development of Blockchain technology, permissioned Blockchains are getting more and more attention from researchers because applications based on permissioned Blockchains are more practical and easier to be carried out. This paper aims to design a dedicated consensus protocol for permissioned Blockchains. The existing consensus protocols applied to permissioned Blockchains are either derived from public Blockchains such as Proof of Work (PoW) or Proof of Stake (PoS), with full decentralization, resulting in low transaction processing efficiency; or derived from traditional Byzantine fault-tolerant (BFT) consensus protocols such as Practical BFT (PBFT) or HoneyBadgerBFT, with high communication complexity of the consensus process, resulting in low scalability. Therefore, we propose a dedicated consensus protocol for permissioned Blockchains called Votes-as-a-Proof (VaaP) with high transaction processing efficiency while ensuring high scalability. Every node in VaaP runs a simple consensus process based on voting in parallel. Faulty nodes will only deprive themselves of using consensus service. We present the comparison of VaaP and Sphinx, one of the state-of-the-art consensus protocols, analytically and experimentally (up to 500 nodes). The results indicate that VaaP outperforms Sphinx in throughput, latency and scalability.

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.

Data security sharing in the Jointcloud has always been a difficult problem for researchers. Some cloud data sharing solutions have a series of problems, such as high communication overhead, difficult key management, data tampering, single cloud failure, etc. We propose a data-sharing mechanism based on blockchain and threshold proxy re-encryption to solve the problems listed above. The proxy re-encryption algorithm we adopted is improved based on the Conditional Proxy Broadcast Re-Encryption. By combining the secret sharing scheme to realize trust decentralization. The re-encryption computing is delivered to multiple cloud service providers, which reduces the centralization problem caused by single cloud processing. At the same time, blockchain is used to manage metadata information and data access control rights to solve data loss and tampering problems. Experimental results show that our mechanism achieves low re-encryption computing overhead.

Summary The blockchain is a distributed ledger that records all transactions and operations in a shared manner. Public blockchains such as Bitcoin realize decentralization at the cost of mining overhead, which is not suitable for real‐life scenarios requiring high throughput. Techniques such as the consortium blockchain improve efficiency through partial decentralization. However, the consensus algorithms used in the existing state‐of‐the‐art consortium blockchains face many challenges when dealing with commercial applications. For example, the high communication overhead hinders the scalability of PBFT‐based consensus algorithms even though they are efficient at small scale. Hashgraph, one of the most popular Directed Acyclic Graph‐based (DAG‐based) consensus algorithms, achieves good performance in scalability; however, it does not allow users' dynamic participation. To deal with these challenges, we propose Jointgraph, a Byzantine fault‐tolerance consensus algorithm for consortium blockchains based on DAG. In Jointgraph, transactions are packed into events and validated by no less than 2/3 of all members. A supervisor is introduced in our design, who monitors member behaviors and improves consensus efficiency. Simulation results demonstrate that Jointgraph outperforms Hashgraph in both throughput and latency.

Summary Sharing trusted data among trusted stakeholders is very important to large‐scale Internet of Things (IoT) applications. However, the entities and organizations involved in IoT naturally lack trusted relationships, which poses significant challenges to the above vision. Specifically, the first challenge is to ensure that the data in the physical world can be objectively and truly injected into the information world of IoT. The second is to ensure the credibility of the entities' identities in IoT. The third is to ensure the authenticity of data, the credibility of identity, and the reliable transmission of data when a third trusted party is unable to provide the expected trusted services. In view of the above challenges, this paper proposes a secure and lightweight triple‐trusting architecture (SLTA), which fully uses a blockchain‐related supporting technology. The architecture includes an oracle‐based data collection mechanism, which ensures that the data collected from edge devices of IoT cannot be modified, and the distributed identity management mechanism, which enhances personal privacy, security, and control of digital identities. Furthermore, a series of innovative designs for applying the blockchain to special large‐scale cooperation scenario in IoT are proposed, which is also a part of the key mechanisms of the SLTA. The innovative design includes a new software‐defined blockchain structure model and a lightweight Byzantine fault‐tolerant algorithm that provides credible support for decentralized data collection, identity management, and data transfer, as well as low‐overhead sequential storage mechanism.

JCLedger is a BlockChain-based distributed ledger for JointCloud that can improve the reliability and convenience of cloud resource exchanges by empowering cooperation among multiple clouds. The biggest challenge for the implementation of JCLedger is the approach to consensus. The existing consensus algorithms for the public BlockChain, such as proof of work (PoW) or proof of stake (PoS) does not apply to the JointCloud, because they require a massive computing power with a low throughput or monopoly risk. In this paper, we propose a practical Byzantine-fault-tolerance (PBFT)-based consensus algorithm called proof of previous transactions (PoPT), in which the accountants are selected by a specific hash function from a certain number of candidates. The candidates are chosen according to the users' participation in JointCloud, and only candidates that join the PBFT-based consensus process instead of all users. We also propose a new BlockChain structure for parallel accounting to improve the scalability of JCLedger, and a consistent hashing algorithm is used to assign the transactions to different accountants. Simulation experiments show that the PoPT can shield the unequal computing power of the users to provide them equal accounting opportunities, and the parallel accounting can handle the massive and high-frequency transactions in JointCloud more efficiently.

Computation offloading has been widely recognized as an effective way to promote the capabilities of resource-constrained mobile devices. The past years have seen a renewed importance of this technology in the emerging field of mobile robots. However, a significant feature of robots compared to traditional mobile computing devices (e.g. smartphones) is that they must collaborate to solve complex tasks in the physical world in many cases, which implies intensive data exchange among the robot peers. This characteristic has not been dealt with in-depth in traditional computation offloading research. In this paper, we propose an approach called Cooperative Offloading, which takes into account the cooperation among mobile devices as well as the communication it brings in computation offloading. Firstly, we propose the offloading decision approach that deals with offloading from a set of robots that cooperate to finish a specific task. Secondly, we present a set of mechanisms to optimize the data transfer path on network topology in multi-robot applications, which can significantly reduce the bandwidth consumption of the robot wireless network. Based on the cooperative offloading approach, we realized a cloud robotics framework called Cloudroid Swarm. Evaluations based on real-life applications have shown that Cloudroid Swarm brings more than five times performance promotion compared to the setup without offloading or with individual computation offloading.

Social media popularity estimation refers to predict the post's popularity using multimodal contents. The prediction performance heavily relies on the feature extraction part and fully leveraging multimodal heterogeneous data is of a great challenge in the practical settings. Despite remarkable progress have been made, most of the previous attempts are restrained from the essentially limited property of the employed single modality. Inspired by the recent success of multimodal learning, we propose a novel multimodal deep learning framework for the popularity prediction task, which aims to leverage the complementary knowledge from different modalities. Moreover, an attention mechanism is introduced in our framework, with the goal to assign large weights to specified modalities during the training and inference phases. To empirically investigate the effectiveness and robustness of the proposed approach, we conduct extensive experiments on the 2020 SMP challenge. The obtained results show that the proposed framework outperforms related approaches.

There have been dramatic increases in industrial investment and growing research interests in Internet of Things over the past few years. Driven by the application demand from Internet of everything in the future, the collaboration between traditional cloud and edge cloud is already on the way to provide better service to diverse users. However, due to the high marginal operation and maintenance costs, there remains a need for collaboration between traditional clouds. JointCloud computing is a new computing paradigm that supports collaboration within heterogeneous clouds, which is a complex problem that is difficult to manage using the traditional methodology of “top-down, decomposition, and reduction” in the benign construction of the JointCloud ecosystem. In this article, we first describe the inherent mechanism behind the constructional evolution of the JointCloud ecosystem, which details the inner workings of its elements and the general principals of how these various elements influence each other. We then propose a series of forward-looking, feasible operational methods to support the constructional evolution of the JointCloud ecosystem that incorporate efficient development methods, data-driven operations, AI-assisted decision making and so on. Finally, a case study of a cross-cloud migration deployment is used to illustrate the scientific basis of the systems inherent mechanism, as well as the efficacy of the supporting methods.

The risk adjusted mortality rate, which is also called standardized mortality ratio (SMR), is one widely used quality measure to evaluate healthcare provider performance. Logistic regression and decision tree are two traditional risk models for mortality rate calculation. Though some machine learning based approaches could achieve higher accuracy, they are hard to interpret and may have poor calibration scores. In this paper, we evaluated multiple machine learning approaches with different formats of longitudinal data, and proposed a hybrid approach based on long short-term memory (LSTM) model and decision tree. The new hybrid method provides a comparable area under the receiver operating characteristic curve (AUC) performance as LSTM with a better calibration score. Using a set of 3,473 patients with 10 months of data from historical, large scale ESRD patient data, the LSTM with long format data approach achieved AUC for prediction of mortality of 0.772 compared to 0.758 for logistic regression and 0.726 for a decision tree model. The hybrid approach could reach 0.783, a little higher than both LSTM and decision tree model. The hybrid approach has the best calibration performance based on the Hosmer Lemeshow test.

Mineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these negative impacts. In this paper, using satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Observation (CALIPSO), we compared several machine learning algorithms to traditional physical models and evaluated their performance regarding mineral dust detection. Based on the comparison results, we proposed a hybrid algorithm to integrate physical model with the data mining model, which achieved the best accuracy result among all the methods. Further, we identified the ranking of different channels of MODIS data based on the importance of the band wavelengths in dust detection. Our model also showed the quantitative relationships between the dust and the different band wavelengths.

JointCloud computing is a new generation cloud computing model based on collaboration among Cloud Service Providers, making resources from multiple clouds deeply integrated., and supporting customize cloud service. To achieve the data confirmation Right when doing data trading to prevent resell is one of the most important challenges faced by such JointCloud environment. In this paper, we propose the JointCloud Computing Data Trading Architecture (JCDTA), an optimized data trading architecture for cross-stakeholder. Firstly, the announced mechanism is used to extract the data resources summary and the owner information into blockchain. Secondly, the untampering of the blockchain is used to record the data resources information, such as data resources statement information, data resources transaction records and data resources operation records. Finally, the supervision mechanism confirms the declaration of the data sources while the encryption technology ensures the privacy of the data resources. JCDTA guarantees the security and the reliability of data trading in the JointCloud environment and ensures the value invariance of data resources.

Cloud computing has completely changed the economics of IT industry. Recently, the new form of "shared global economy" requires cloud services to be collaboratively provisioned by different cloud providers in a Geo-distributed manner, which brings severe challenges in service performance and cost. To address this problem, in this paper we propose JointCloud, a cross-cloud cooperation architecture for integrated Internet service customization. JointCloud borrows the idea from airline alliances and aims at empowering the cooperation among multiple clouds to provide efficient cross-cloud services. JointCloud focuses not only on the vertical integration of cloud resources but also on the horizontal cooperation among different cloud vendors. This paper describes the concept and architecture of JointCloud, as well as the initial design of the key components, namely, communication, storage, and computation.